QC

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        Note that additional data was saved in multiqc_report_data when this report was generated.


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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        About MultiQC

        This report was generated using MultiQC, version 1.20

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        QC

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2024-05-09, 10:07 EDT based on data in:


        General Statistics

        Showing 28/28 rows and 16/38 columns.
        Sample NameM Input readsUnmapDupProp pairMed ISVariantsSex% GCInsert SizeDuplicationM ReadsMedian CoverageBases ≥ 30X% Dups% GCM Seqs
        SC501095
        2555.0 M
        3.8%
        4.5%
        94.0%
        332
        4849223
        XY
        41%
        346 bp
        4.3%
        89.0X
        93%
        SC501095_B22CHLTLT3.R1
        17.6%
        40%
        1118.0 M
        SC501095_B22CHLTLT3.R2
        18.7%
        40%
        1118.0 M
        SC501096
        2380.0 M
        3.4%
        4.4%
        94.3%
        369
        4904207
        XX
        41%
        384 bp
        4.0%
        86.0X
        93%
        SC501096_B22CHLTLT3.R1
        16.7%
        40%
        1026.9 M
        SC501096_B22CHLTLT3.R2
        16.4%
        40%
        1026.9 M
        SC501105
        2617.6 M
        3.4%
        7.3%
        94.7%
        302
        4885301
        XX
        41%
        324 bp
        7.0%
        86.0X
        93%
        SC501105_B22CHLTLT3.R1
        18.1%
        40%
        1150.9 M
        SC501105_B22CHLTLT3.R2
        19.4%
        40%
        1150.9 M
        SC501108
        2465.7 M
        3.5%
        6.8%
        94.1%
        380
        4867227
        XY
        41%
        376 bp
        6.2%
        83.0X
        93%
        SC501108_B22CHLTLT3.R1
        18.5%
        40%
        1044.0 M
        SC501108_B22CHLTLT3.R2
        18.8%
        40%
        1044.0 M
        SC501110
        2223.9 M
        3.2%
        8.3%
        94.4%
        347
        4884094
        XX
        41%
        362 bp
        7.8%
        76.0X
        92%
        SC501110_B22CHLTLT3.R1
        16.7%
        40%
        974.8 M
        SC501110_B22CHLTLT3.R2
        16.6%
        40%
        974.8 M
        SC501111
        2514.8 M
        3.5%
        11.9%
        93.9%
        385
        4844316
        XY
        41%
        386 bp
        11.2%
        85.0X
        93%
        SC501111_B22CHLTLT3.R1
        20.6%
        40%
        1096.7 M
        SC501111_B22CHLTLT3.R2
        20.5%
        40%
        1096.7 M
        SD162355
        2694.6 M
        3.7%
        10.4%
        93.8%
        326
        4896047
        XY
        41%
        327 bp
        10.1%
        87.0X
        93%
        SD162355_A22FKHJLT3.R1
        21.0%
        41%
        1196.4 M
        SD162355_A22FKHJLT3.R2
        22.4%
        41%
        1196.4 M
        SD162357
        2583.1 M
        3.4%
        18.1%
        94.2%
        390
        4877362
        XY
        42%
        380 bp
        17.8%
        82.0X
        93%
        SD162357_A22FKHJLT3.R1
        23.3%
        41%
        1157.1 M
        SD162357_A22FKHJLT3.R2
        24.5%
        41%
        1157.1 M
        SD162359
        2751.1 M
        3.2%
        9.6%
        94.8%
        296
        4946727
        XX
        42%
        320 bp
        9.5%
        90.0X
        93%
        SD162359_A22FKHJLT3.R1
        19.6%
        41%
        1240.9 M
        SD162359_A22FKHJLT3.R2
        21.0%
        41%
        1240.9 M
        quality_yield
        2481.7 M

        DRAGEN

        DRAGEN is a Bio-IT Platform that provides ultra-rapid secondary analysis of sequencing data using field-programmable gate array technology (FPGA).

        Mapping metrics

        Mapping metrics, similar to the metrics computed by the samtools-stats command. Shown on per read group level. To see per-sample level metrics, refer to the general stats table.

        Showing 9/9 rows and 12/71 columns.
        Sample NameM Input readsPairedQC-failUnmapDupProp pairDiscordSingletonDiff chr, MQ⩾10Med ISM AlignmentsSec'ry
        SC501095
        2555.0 M
        100.0%
        0.00%
        3.8%
        4.5%
        94.0%
        1.22%
        0.94%
        0.85%
        332
        2467.2 M
        0.00%
        SC501096
        2380.0 M
        100.0%
        0.00%
        3.4%
        4.4%
        94.3%
        1.30%
        1.07%
        0.92%
        369
        2307.7 M
        0.00%
        SC501105
        2617.6 M
        100.0%
        0.00%
        3.4%
        7.3%
        94.7%
        1.05%
        0.87%
        0.74%
        302
        2537.8 M
        0.00%
        SC501108
        2465.7 M
        100.0%
        0.00%
        3.5%
        6.8%
        94.1%
        1.32%
        1.10%
        0.82%
        380
        2386.9 M
        0.00%
        SC501110
        2223.9 M
        100.0%
        0.00%
        3.2%
        8.3%
        94.4%
        1.40%
        0.96%
        0.95%
        347
        2159.4 M
        0.00%
        SC501111
        2514.8 M
        100.0%
        0.00%
        3.5%
        11.9%
        93.9%
        1.50%
        1.09%
        1.06%
        385
        2433.9 M
        0.00%
        SD162355
        2694.6 M
        100.0%
        0.00%
        3.7%
        10.4%
        93.8%
        1.57%
        0.95%
        1.21%
        326
        2606.5 M
        0.00%
        SD162357
        2583.1 M
        100.0%
        0.00%
        3.4%
        18.1%
        94.2%
        1.43%
        0.91%
        1.08%
        390
        2503.2 M
        0.00%
        SD162359
        2751.1 M
        100.0%
        0.00%
        3.2%
        9.6%
        94.8%
        1.11%
        0.80%
        0.83%
        296
        2670.8 M
        0.00%

        Mapped / paired / duplicated

        Distribution of reads based on pairing, duplication and mapping.

        Created with MultiQC

        Variant calling

        Variant calling metrics. Metrics are reported for each sample in multi sample VCF and gVCF files. Based on the run case, metrics are reported either as standard VARIANT CALLER or JOINT CALLER. All metrics are reported for post-filter VCFs, except for the "Filtered" metrics which represent how many variants were filtered out from pre-filter VCF to generate the post-filter VCF.

        Showing 9/9 rows and 9/31 columns.
        Sample NameVariantsMultiallelicSNPInsDelTi/TvHet/HomCallabilityM VC reads
        SC501095
        4849223
        1.9%
        80.4%
        9.3%
        9.3%
        2.0
        1.5
        NA
        2316.1 M
        SC501096
        4904207
        1.9%
        80.4%
        9.5%
        9.5%
        2.0
        1.6
        NA
        2170.4 M
        SC501105
        4885301
        1.9%
        80.4%
        9.5%
        9.5%
        2.0
        1.6
        NA
        2311.6 M
        SC501108
        4867227
        1.9%
        80.5%
        9.3%
        9.3%
        2.0
        1.6
        NA
        2181.9 M
        SC501110
        4884094
        1.9%
        80.4%
        9.5%
        9.5%
        2.0
        1.6
        NA
        1947.3 M
        SC501111
        4844316
        1.9%
        80.4%
        9.3%
        9.3%
        2.0
        1.6
        NA
        2096.2 M
        SD162355
        4896047
        1.9%
        80.4%
        9.3%
        9.3%
        2.0
        1.6
        NA
        2281.8 M
        SD162357
        4877362
        1.9%
        80.4%
        9.3%
        9.3%
        2.0
        1.6
        NA
        1993.3 M
        SD162359
        4946727
        2.0%
        80.5%
        9.5%
        9.5%
        2.0
        1.6
        NA
        2356.1 M

        Target Bed Coverage Metrics

        Coverage metrics over target Bed. All samples are based on the target_bed.

        Press the Help button for details.

        The following criteria are used when calculating coverage:

        • Duplicate reads and clipped bases are ignored.

        • DRAGEN V3.4 - 3.7: Only reads with MAPQ > min MAPQ and bases with BQ > min BQ are considered

        • DRAGEN V3.8 - 4.1: By default, reads with MAPQ < 1 and bases with BQ < 0 are ignored. You can use the qc-coverage-filters-n option to specify which BQ bases and MAPQ reads to filter out.

        Considering only bases usable for variant calling, i.e. excluding:

        1. Clipped bases

        2. Bases in duplicate reads

        3. Reads with MAPQ < min MAPQ (default 20)

        4. Bases with BQ < min BQ (default 10)

        5. Reads with MAPQ = 0 (multimappers)

        6. Overlapping mates are double-counted

        Each _coverage_metrics.csv file may have an associated _overall_mean_cov.csv file. The latter contains the 'Average alignment coverage over <source file>' metric. Information about <source file>s can be found in the section's description or in this drop-list below if the produced text is long. If input directory does not contain _overall_mean_cov files, then "No 'coverage bed/target bed/wgs' source file found" is printed.

        Showing 9/9 rows and 5/36 columns.
        Sample NameM Aln readsMb Aln basesDepthUniformity(>0.2×mean)Mean/med autosomal coverage
        SC501095
        2206.5
        314731.2
        107.13 x
        97.15 %
        0.97 x
        SC501096
        2083.9
        303304.7
        103.24 x
        96.63 %
        0.96 x
        SC501105
        2208.2
        312665.2
        106.41 x
        96.61 %
        0.97 x
        SC501108
        2082.2
        300705.4
        102.28 x
        97.17 %
        0.97 x
        SC501110
        1859.5
        268686.0
        91.51 x
        96.62 %
        0.97 x
        SC501111
        2001.8
        290192.3
        98.72 x
        97.20 %
        0.97 x
        SD162355
        2178.1
        308900.6
        104.90 x
        97.10 %
        0.97 x
        SD162357
        1905.5
        276178.1
        93.66 x
        97.18 %
        0.97 x
        SD162359
        2265.5
        320819.8
        108.75 x
        96.54 %
        0.97 x

        WGS Coverage Metrics

        Coverage metrics over genome. All samples are based on the wgs.

        Press the Help button for details.

        The following criteria are used when calculating coverage:

        • Duplicate reads and clipped bases are ignored.

        • DRAGEN V3.4 - 3.7: Only reads with MAPQ > min MAPQ and bases with BQ > min BQ are considered

        • DRAGEN V3.8 - 4.1: By default, reads with MAPQ < 1 and bases with BQ < 0 are ignored. You can use the qc-coverage-filters-n option to specify which BQ bases and MAPQ reads to filter out.

        Considering only bases usable for variant calling, i.e. excluding:

        1. Clipped bases

        2. Bases in duplicate reads

        3. Reads with MAPQ < min MAPQ (default 20)

        4. Bases with BQ < min BQ (default 10)

        5. Reads with MAPQ = 0 (multimappers)

        6. Overlapping mates are double-counted

        Each _coverage_metrics.csv file may have an associated _overall_mean_cov.csv file. The latter contains the 'Average alignment coverage over <source file>' metric. Information about <source file>s can be found in the section's description or in this drop-list below if the produced text is long. If input directory does not contain _overall_mean_cov files, then "No 'coverage bed/target bed/wgs' source file found" is printed.

        Showing 9/9 rows and 5/36 columns.
        Sample NameM Aln readsMb Aln basesDepthUniformity(>0.2×mean)Mean/med autosomal coverage
        SC501095
        2206.5
        314731.2
        104.10 x
        94.23 %
        0.96 x
        SC501096
        2083.9
        303304.7
        100.34 x
        93.70 %
        0.96 x
        SC501105
        2208.2
        312665.2
        103.41 x
        93.69 %
        0.97 x
        SC501108
        2082.2
        300705.4
        99.47 x
        94.27 %
        0.97 x
        SC501110
        1859.5
        268686.0
        88.91 x
        93.77 %
        0.96 x
        SC501111
        2001.8
        290192.3
        95.98 x
        94.25 %
        0.96 x
        SD162355
        2178.1
        308900.6
        102.13 x
        94.10 %
        0.97 x
        SD162357
        1905.5
        276178.1
        91.37 x
        94.24 %
        0.97 x
        SD162359
        2265.5
        320819.8
        106.05 x
        93.55 %
        0.97 x

        Coverage distribution

        Number of locations in the reference genome with a given depth of coverage.

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).

        Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.

        If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).

        This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).

        Created with MultiQC

        Cumulative coverage hist

        Number of locations in the reference genome with at least given depth of coverage.

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

        Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

        For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

        Created with MultiQC

        Coverage per contig

        Average coverage per contig or chromosome. Calculated as the number of bases (excluding duplicate marked reads, reads with MAPQ=0, and clipped bases), divided by the length of the contig or (if a target bed is used) the total length of the target region spanning that contig.

        Created with MultiQC

        Coverage per contig (non-main)

        Non-main contigs: unlocalized (random), unplaced (chrU), alts (*_alt), mitochondria (chrM), EBV, HLA. Zoom in to see more contigs as all labels don't fit the screen.

        Created with MultiQC

        Fragment length hist

        Distribution of estimated fragment lengths of mapped reads per read group. Only points supported by at least 5 reads are shown to prevent long flat tail. The plot is also smoothed down to showing 300 points on the X axis to reduce noise.

        Created with MultiQC

        Trimmer Metrics

        Metrics on trimmed reads.

        Showing 9/9 rows and 22/22 columns.
        Sample NameTotal input readsTotal input basesTotal input bases R1Total input bases R2Average input read lengthTotal trimmed readsTotal trimmed basesAverage bases trimmed per readAverage bases trimmed per trimmed readRemaining poly-G K-mers R1 3primeRemaining poly-G K-mers R2 3primePoly-G soft trimmed reads unfiltered R1 3primePoly-G soft trimmed reads unfiltered R2 3primePoly-G soft trimmed reads filtered R1 3primePoly-G soft trimmed reads filtered R2 3primePoly-G soft trimmed bases unfiltered R1 3primePoly-G soft trimmed bases unfiltered R2 3primePoly-G soft trimmed bases filtered R1 3primePoly-G soft trimmed bases filtered R2 3primeTotal filtered readsReads filtered for minimum read length R1Reads filtered for minimum read length R2
        SC501095
        2554951260.0
        365659479246.0
        182671015190.0
        182988464056.0
        143.0
        0.0
        0.0
        0.0
        0.0
        0.0
        1.0
        0.1
        1.5
        0.0
        0.0
        0.1
        1.4
        0.0
        0.0
        0.0
        0.0
        0.0
        SC501096
        2380036766.0
        348283330343.0
        174049534906.0
        174233795437.0
        146.0
        0.0
        0.0
        0.0
        0.0
        0.0
        1.1
        0.1
        1.6
        0.0
        0.0
        0.0
        1.5
        0.0
        0.0
        0.0
        0.0
        0.0
        SC501105
        2617623186.0
        372355127256.0
        185951497273.0
        186403629983.0
        142.0
        0.0
        0.0
        0.0
        0.0
        0.0
        1.0
        0.1
        1.5
        0.0
        0.0
        0.0
        1.4
        0.0
        0.0
        0.0
        0.0
        0.0
        SC501108
        2465729128.0
        358404014957.0
        179019948420.0
        179384066537.0
        145.0
        0.0
        0.0
        0.0
        0.0
        0.0
        1.1
        0.1
        1.6
        0.0
        0.0
        0.0
        1.5
        0.0
        0.0
        0.0
        0.0
        0.0
        SC501110
        2223890036.0
        323400398399.0
        161614492216.0
        161785906183.0
        145.0
        0.0
        0.0
        0.0
        0.0
        0.0
        0.9
        0.1
        1.4
        0.0
        0.0
        0.0
        1.3
        0.0
        0.0
        0.0
        0.0
        0.0
        SC501111
        2514803832.0
        366659342143.0
        182966652446.0
        183692689697.0
        145.0
        0.0
        0.0
        0.0
        0.0
        0.0
        1.4
        0.1
        1.9
        0.0
        0.0
        0.0
        1.8
        0.0
        0.0
        0.0
        0.0
        0.0
        SD162355
        2694630052.0
        384892728308.0
        192024215197.0
        192868513111.0
        142.0
        0.0
        0.0
        0.0
        0.0
        0.0
        1.1
        0.1
        1.9
        0.0
        0.0
        0.1
        1.7
        0.0
        0.0
        0.0
        0.0
        0.0
        SD162357
        2583116404.0
        377045043113.0
        188018447976.0
        189026595137.0
        145.0
        0.0
        0.0
        0.0
        0.0
        0.0
        1.4
        0.1
        1.9
        0.0
        0.0
        0.1
        1.8
        0.0
        0.0
        0.0
        0.0
        0.0
        SD162359
        2751117024.0
        392305678023.0
        195972751622.0
        196332926401.0
        142.0
        0.0
        0.0
        0.0
        0.0
        0.0
        0.8
        0.1
        1.4
        0.0
        0.0
        0.1
        1.3
        0.0
        0.0
        0.0
        0.0
        0.0

        Time Metrics

        Time metrics for DRAGEN run. Total run time is less than the sum of individual steps because of parallelization.

        Created with MultiQC

        DRAGEN-FastQC

        DRAGEN-FastQC is a Bio-IT Platform that provides ultra-rapid secondary analysis of sequencing data using field-programmable gate array technology (FPGA).

        Per-Position Mean Quality Scores

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per-Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help: The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Per-Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help: This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content. In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution. An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        GC Content Mean Quality Scores

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per-Position N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help: If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called. It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Per-Position Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor. To see the data as a line plot, as in the original FastQC graph, click on a sample track. From the FastQC help: Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called. In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other. It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Picard

        Picard is a set of Java command line tools for manipulating high-throughput sequencing data.

        Alignment Summary

        Please note that Picard's read counts are divided by two for paired-end data. Total bases (including unaligned) is not provided.

        Created with MultiQC

        Mean read length

        The mean read length of the set of reads examined.

        Created with MultiQC

        Base Distribution

        Plot shows the distribution of bases by cycle.

        Created with MultiQC

        GC Coverage Bias

        This plot shows bias in coverage across regions of the genome with varying GC content. A perfect library would be a flat line at y = 1.

        Created with MultiQC

        Insert Size

        Plot shows the number of reads at a given insert size. Reads with different orientations are summed.

        Created with MultiQC

        Mark Duplicates

        Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

        The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

        To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

        • READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
        • READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
        • READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
        • READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
        • READS_UNMAPPED = UNMAPPED_READS
        Created with MultiQC

        Mean Base Quality by Cycle

        Plot shows the mean base quality by cycle.

        This metric gives an overall snapshot of sequencing machine performance. For most types of sequencing data, the output is expected to show a slight reduction in overall base quality scores towards the end of each read.

        Spikes in quality within reads are not expected and may indicate that technical problems occurred during sequencing.

        Created with MultiQC

        Base Quality Distribution

        Plot shows the count of each base quality score.

        Created with MultiQC

        WGS Coverage

        The number of bases in the genome territory for each fold coverage. Note that final 1% of data is hidden to prevent very long tails.

        Created with MultiQC

        WGS Filtered Bases

        For more information about the filtered categories, see the Picard documentation.

        Created with MultiQC

        FastQ Screen

        Version: 0.15.3

        FastQ Screen allows you to screen a library of sequences in FastQ format against a set of sequence databases so you can see if the composition of the library matches with what you expect.DOI: 10.12688/f1000research.15931.2.

        Mapped Reads

        Created with MultiQC

        FastQC

        Version: 0.12.1

        FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        18 samples had less than 1% of reads made up of overrepresented sequences

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 0/0 rows.
        Overrepresented sequence

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        SoftwareVersion
        FastQ Screen0.15.3
        FastQC0.12.1